Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliaryads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction.

中文翻译：

---

基于注意力机制的深度上下文交互网络用于点击率预测

点击率 (CTR) 预测旨在预测用户点击广告的概率，是在线广告系统中的一项关键任务。这个问题非常具有挑战性，因为（1）有效的预测依赖于高阶组合特征，以及（2）与可能影响点击率的辅助广告的关系。在本文中，我们提出了注意力机制上的深度上下文交互网络（DCIN-Attention）来同时处理特征交互和上下文。上下文包括当前搜索页面中的其他广告，用户历史上点击过和未点击过的广告。具体来说，我们使用注意力机制来学习目标广告和每种类型的辅助广告之间的交互。残差网络用于对低维空间中的特征交互进行建模，并且使用多头自注意力神经网络，可以对高阶特征交互进行建模。在 Avito 数据集上的实验结果表明 DCIN 优于几种现有的 CTR 预测方法。